Abstract
With the increasing concern and challenges that power utilities are facing with managing the risk of aging equipment. Power utilities are turning towards statistical analysis in order to apply system reliability studies to mitigate and manage the risk of customer interruptions, equipment failure, system collapse, as well as to improve system performance. Because of the scarcity of end-of-life data from power system equipment, the simple mean life cannot be employed because it only uses information from dead equipment. As a result, statistical analysis is utilized to combine both surviving and dead components, as both contribute to the equipment's overall mean life. The first method presented in this paper is derived using a set of basic calculation methods, whereas for the Weibull distribution, an optimization technique is utilized to obtain estimates of the mean, standard deviation, shape, and scale parameters. Utilizing MATLAB software two distinct equipment sample groups are gathered to estimate the end-of-life of each equipment group using both methods.